Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens.

K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250).

As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Overall, 'optimized' methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.Īntibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. In the present work, we describe these new methods and the benchmarking of their performances.īenchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments.

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